OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction

2026-04-28

AI Summary

This work addresses key challenges in complex industrial settings—namely, poor scalability, limited observability, and difficulties in autonomous evolution—faced by multi-agent systems. To overcome these limitations, the authors propose OxyGent, a novel framework that introduces a unified Oxy abstraction to encapsulate agents, tools, large language models, and reasoning pipelines as plug-and-play atomic components. This design enables LEGO-like composition, non-intrusive monitoring, and continuous system evolution. Core innovations include a permission-driven dynamic planning mechanism that replaces rigid workflows, automatic execution graph generation, and the OxyBank platform, which facilitates automatic feedback, annotation, and co-evolution of AI assets. Empirical results demonstrate that OxyGent substantially enhances the robustness, flexibility, and scalability of multi-agent systems.

Abstract

Deploying production-ready multi-agent systems (MAS) in complex industrial environments remains challenging due to limitations in scalability, observability, and autonomous evolution. We present OxyGent, an open-source framework that enables modular, observable, and evolvable MAS via a unified Oxy abstraction, in which agents, tools, LLMs, and reasoning flows are encapsulated as pluggable atomic components. This Lego-like assembly paradigm supports scalable system composition and non-intrusive monitoring. To enhance observability, OxyGent introduces permission-driven dynamic planning that replaces rigid workflows with execution graphs generated at runtime, which provide adaptive visualizations. To support continuous evolution, the framework integrates OxyBank, an AI asset management platform that supports automated data backflow, annotation, and joint evolution. Empirical evaluations and real-world case studies show that OxyGent provides a robust and scalable foundation for MAS. OxyGent is publicly available at https://oxygent.jd.com/.

Problem

• multi-agent systems
• scalability
• observability
• autonomous evolution
• industrial deployment

Innovation

• modular multi-agent systems
• Oxy abstraction
• permission-driven dynamic planning
• observable execution graphs
• AI asset evolution